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Linear Regression

Strongly Recommended Prerequisites

Recommended Prerequisites

Linear regression is a topic that one will encounter many times and in many different places in data science as a didactic precursor to more advanced techniques. If you're actually going to use linear regression in practice, you should have a dedicated resource, as there are many practical complications and solutions to those complications that you will not learn from a more general book.

Recommended Books

Introduction to Linear Regression Analysis

Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

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Key Features

In-text exercises

Solution manual available

R and SAS code examples

Key Topics

Box-Cox Transformation

Confidence Intervals

Diagnostics

Generalized Linear Models

Hypothesis Testing

Least-Squares Estimation

Leverage and Influence

Logistic Regression

Maximum-Likelihood Estimation

Model Adequacy Checking

Model Validation

Multicollinearity

Multiple Linear Regression

Nonlinear Regression

Nonparametric Regression

Outliers

PRESS Statistic

Poisson Regression

Polynomial Regression

Prediction

Random Regressors

Residual Analysis

Robust Regression

Simple Linear Regression

Time Series

Transformations

Variable Selection

Variance-Stabilizing Transformations

Weighted Least-Squares

Description

This book gives a fairly standard introduction to simple and multiple linear regression, and then it devotes most of the text to dealing with their practical problems. Detecting and dealing with multicolinearity and outliers as well as many diagnostics and other practical topics occupy the majority of the book. Generalized linear models are introduced, but they really need their own treatment (we recommend some here ).